mppGE_proc: MPP GxE QTL analysis

In vincentgarin/mppR: Multi-Parent Population QTL Analysis

MPP GxE QTL analysis

Description

QTL detection in MPP characterized in multiple environments.

Usage

mppGE_proc(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait,
  EnvNames = NULL,
  VCOV = "UN",
  ref_par = NULL,
  VCOV_data = "unique",
  SIM_only = FALSE,
  thre.cof = 4,
  win.cof = 50,
  cof_red = FALSE,
  cof_pval_sign = 0.1,
  window = 20,
  thre.QTL = 4,
  win.QTL = 20,
  text.size = 18,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100,
  verbose = TRUE,
  output.loc = NULL
)

Arguments

pop.name	Character name of the studied population. Default = "MPP".
trait.name	Character name of the studied trait. Default = "trait1".
mppData	An object of class mppData.
trait	Character vector specifying which traits (environments) should be used.
EnvNames	character expression indicating the environment or trait labels. By default: Env_1, 2, 3, etc.
VCOV	VCOV Character expression defining the type of variance covariance structure used. 'CS' for compound symmetry assuming a unique genetic covariance between environments. 'CSE' for cross-specific within environment error term. 'CS_CSE' for both compound symmetry plus cross-specific within environment error term. 'UN' for unstructured environmental variance covariance structure allowing a specific genotypic covariance for each pair of environments. Default = 'UN'
ref_par	Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL
VCOV_data	Character specifying if the reference VCOV of the CIM profile computation should be formed taking all cofactors into consideration ("unique") or if different VCOVs should be formed by removing the cofactor information that is too close of a tested cofactor position ("minus_cof"). Default = "unique"
SIM_only	Logical value specifying if the procedure should only calculate a SIM profile (no CIM). This option can be used with large dataset to save time. Default = FALSE
thre.cof	Numeric value representing the -log10(p-value) threshold above which a position can be selected as cofactor. Default = 4.
win.cof	Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50.
cof_red	Logical value specifying if the cofactor matrix should be reduced by only keeping the significant allele by environment interaction. Default = FALSE
cof_pval_sign	Numeric value specifying the p-value significance of an allele by environment term to be kept in the model. Default = 0.1
window	Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.
thre.QTL	Numeric value representing the -log10(p-value) threshold above which a position can be selected as QTL. Default = 4.
win.QTL	Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.
text.size	Numeric value specifying the size of graph axis text elements. Default = 18.
n.cores	Numeric. Specify here the number of cores you like to use. Default = 1.
maxIter	maximum number of iterations for the lme optimization algorithm. Default = 100.
msMaxIter	maximum number of iterations for the optimization step inside the lme optimization. Default = 100.
verbose	Logical value indicating if the steps of mpp_proc should be printed. Default = TRUE.
output.loc	Path where a folder will be created to save the results. Default = NULL.

Details

The procedure is the following:

1. Simple interval mapping (SIM) to select cofactors (mppGE_SIM).

2. Composite interval mapping (CIM) with selected cofactors (mppGE_CIM).

3. Estimation of QTLs additive allelic effect (QTL_effect_GE).

4. Estimation of the global QTLs effect R squared and individual QTL effect R squared (QTL_R2_GE).

Value

Return:

List containing the following items:

n.QTL	Number of detected QTLs.
cofactors	Data.frame with cofactors positions.
QTL	Data.frame with QTL positions.
Q_eff	list containing the estimated QTL allelic effects.
R2	List containing R squared statistics of the QTL effects

Some output files are also saved at the specified location (output.loc):

1. The SIM and CIM results in a RData file (SIM.RData, CIM.RData).

2. The list of cofactors (cofactors.RData).

3. The list of QTL (QTLs.RData).

4. The list of QTL allelic effects (QTL_effects.RData).

5. The QTL R squared statistics (QTL_R2.RData)

6. The number of detected QTLs and adjusted R2 (Glb_res.RData)

7. The plot of the CIM profile (QTL_profile.pdf) with dotted vertical lines representing the cofactors positions and the plot of the genetic effects per cross or parents obtained with plot_allele_eff_GE (gen_eff.pdf) with dashed lines representing the QTL positions.

Author(s)

Vincent Garin

See Also

mppGE_CIM, mppGE_SIM, QTL_effect_GE, QTL_R2_GE

Examples

## Not run: 

data(mppData_GE)

MPP_GE_QTL <- mppGE_proc(pop.name = 'EUNAM', trait.name = 'DMY',
mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
n.cores = 1, output.loc = tempdir())


## End(Not run)

vincentgarin/mppR documentation built on March 13, 2024, 7:30 p.m.